PhD / This thesis is concerned with planning and acting under uncertainty in partially-observable continuous domains. In particular, it focusses on the problem of mobile robot navigation given a known map. The dominant paradigm for robot localisation is to use Bayesian estimation to maintain a probability distribution over possible robot poses. In contrast, control algorithms often base their decisions on the assumption that a single state, such as the mode of this distribution, is correct. In scenarios involving significant uncertainty, this can lead to serious control errors. It is generally agreed that the reliability of navigation in uncertain environments would be greatly improved by the ability to consider the entire distribution when acting, rather than the single most likely state. The framework adopted in this thesis for modelling navigation problems mathematically is the Partially Observable Markov Decision Process (POMDP). An exact solution to a POMDP problem provides the optimal balance between reward-seeking behaviour and information-seeking behaviour, in the presence of sensor and actuation noise. Unfortunately, previous exact and approximate solution methods have had difficulty scaling to real applications. The contribution of this thesis is the formulation of an approach to planning in the space of continuous parameterised approximations to probability distributions. Theoretical and practical results are presented which show that, when compared with similar methods from the literature, this approach is capable of scaling to larger and more realistic problems. In order to apply the solution algorithm to real-world problems, a number of novel improvements are proposed. Specifically, Monte Carlo methods are employed to estimate distributions over future parameterised beliefs, improving planning accuracy without a loss of efficiency. Conditional independence assumptions are exploited to simplify the problem, reducing computational requirements. Scalability is further increased by focussing computation on likely beliefs, using metric indexing structures for efficient function approximation. Local online planning is incorporated to assist global offline planning, allowing the precision of the latter to be decreased without adversely affecting solution quality. Finally, the algorithm is implemented and demonstrated during real-time control of a mobile robot in a challenging navigation task. We argue that this task is substantially more challenging and realistic than previous problems to which POMDP solution methods have been applied. Results show that POMDP planning, which considers the evolution of the entire probability distribution over robot poses, produces significantly more robust behaviour when compared with a heuristic planner which considers only the most likely states and outcomes.

The main theme of this research will be to develop solutions to the widely known 3-part question in mobile robotics comprising of "Where am I" "Where should I be" and "How do I get there". This can be achieved by implementing automation algorithms. Automation algorithms or control algorithms are vital components of any autonomous vehicle. Design and development of both prototype and full-scale control algorithms for a Long-Airdox Full Dimension Continuous Haulage system will be the main focus. Automation is a highly complex task, which aims at achieving increased levels of equipment efficiency by eliminating errors that arise due to human interference. Achieving a fully autonomous operation of a machine involves a variety of high-level interlaced functions that work in harmony, and at the same time perform functions that mimic the human operator. Automation has expanded widely in the field of mobile robotics, thus leading to the development of autonomous robots, automated guided vehicles and other autonomous vehicles. An indispensable element of an autonomous vehicle is a navigation system that steers it to a required destination. The vehicle must be able to determine its relationship to the environment by sensing, and also must be able to decide what actions are required to achieve its goal(s) in the working environment. The goal of this research is to demonstrate a fully autonomous operation of the Continuous Haulage System, and to establish its potential advantages. / Master of Science

This thesis investigates the problem of sensors used with mobile robots. Firstly, a colour sensor is considered, for its ability to detect objects having the three primary colours Red, Green and Blue (RGB). Secondly, an accelerometer was investigated, from which velocity was derived from the raw data using numerical integration. The purpose of the design and development of the sensors was to use them for robotic navigation and collision avoidance. This report presents the results of experiments carried out on the colour sensor and the accelerometer. A discussion of the results and some conclusions are also presented. It proved feasible to achieve the goal of detecting colours successfully but only for a limited distance. The accelerometer proved reliable but is not yet being applied in real time. Both the colour sensor and the accelerometer proved to be inexpensive. Some recommendations are made to improve both the colour sensor and the accelerometer sensors.

Haul truck drivers frequently have difficulties spotting beside shovels. This is typically a combination of reduced visibility and poor mining conditions. Based on first-hand data collected from the Goldstrike Open Pit, it was learned that, on average, 9% of all spotting actions required corrective movements to facilitate loading. This thesis investigates an automated solution to haul truck spotting that does not rely on the use of the satellite global positioning system (GPS), since GPS can perform unreliably. This thesis proposes that if spotting was automated, a significant decrease in cycle times could result. Using conventional algorithms and techniques from the field of mobile robotics, vehicle pose estimation and control algorithms were designed to enable autonomous shovel-truck spotting. The developed algorithms were verified by using both simulation and field testing with real hardware. Tests were performed in analog conditions on an automation-ready Kubota RTV 900 utility truck. When initiated from a representative pose, the RTV successfully spotted to the desired location (within 1 m) in 95% of the conducted trials. The results demonstrate that the proposed approach is a strong candidate for an auto-spot system. / Thesis (Master, Mining Engineering) -- Queen's University, 2013-01-28 09:49:20.584

Localization and motion control (navigation) are two major tasks for a successful mobile robot navigation. The motion controller determines the appropriate action for the robot’s actuator based on its current state in an operating environment. A robot recognizes its environment through some sensors and executes physical actions through actuation mechanisms. However, sensory information is noisy and hence actions generated based on this information may be non-deterministic. Therefore, a mobile robot provides actions to its actuators with a certain degree of uncertainty. Moreover, when no prior knowledge of the environment is available, the problem becomes even more difficult, as the robot has to build a map of its surroundings as it moves to determine the position. Skilled navigation of a differential drive mobile robot (DDMR) requires solving these tasks in conjunction, since they are inter-dependent. Having resolved these tasks, mobile robots can be employed in many contexts in indoor and outdoor environments such as delivering payloads in a dynamic environment, building safety, security, building measurement, research, and driving on highways. This dissertation exploits the use of the emerging Radio Frequency IDentification (RFID) technology for the design and implementation of cost-effective and modular control techniques for navigating a mobile robot in an indoor environment. A successful realization of this process has been addressed with three separate navigation modules. The first module is devoted to the development of an indoor navigation system with a customized RFID reader. This navigation system is mainly pioneered by mounting a multiple antenna RFID reader on the robot and placing the RFID tags in three dimensional workspace, where the tags’ orthogonal position on the ground define the desired positions that the robot is supposed to reach. The robot generates control actions based on the information provided by the RFID reader for it to navigate those pre-defined points. On the contrary, the second and third navigation modules employ custom-made RFID tags (instead of the RFID reader) which are attached at different locations in the navigation environment (on the ceiling of an indoor office, or on posts, for instance). The robot’s controller generates appropriate control actions for it’s actuators based on the information provided by the RFID tags in order to reach target positions or to track pre-defined trajectory in the environment. All three navigation modules were shown to have the ability to guide a mobile robot in a highly reverberant environment with variant degrees of accuracy.

A integração de sistemas de Visão Computacional com a Robótica Móvel é um campo de grande interesse na pesquisa. Este trabalho demonstra um método de localização global para Robôs Móveis Autônomos baseado na criação de uma memória visual, através da detecção e descrição de pontos de referência de imagens capturadas, com o método SURF, associados a dados de odometria, em um ambiente interno. O procedimento proposto, associado com conhecimento específico sobre o ambiente, permite que a localização seja obtida posteriormente pelo pareamento entre quadros memorizados e a cena atual observada pelo robô. Experimentos são conduzidos para mostrar a efetividade do método na localização robótica. Aprimoramentos para situações difíceis como travessia de portas são apresentados. Os resultados são analisados, e alternativas para navegação e possíveis futuros refinamentos discutidos / Integration of Computer Vision and Mobile Robotics systems is a field of great interest in research. This work demonstrates a method of global localization for Autonomous Mobile Robots based on the creation of a visual memory map, through detection and description of reference points from captured images, using the SURF method, associated to odometer data in a specific environment. The proposed procedure, coupled with specific knowledge of the environment, allows for localization to be achieved at a later stage through pairing of these memorized features with the scene being observed in real time. Experiments are conducted to show the effectiveness of the method for the localization of mobile robots in indoor environments. Improvements aimed at difficult situations such as traversing doors are presented. Results are analyzed and navigation alternatives and possible future refinements are discussed

nÃo hà / Middleware à a camada de software, situada entre o sistema operacional e a camada de aplicaÃÃes ou entre camadas de aplicaÃÃes, que fornece uma infraestrutura para integraÃÃo de programas aplicativos e dados em sistema de processamento distribuÃdo. Nesta tese propomos uma nova camada de software (Middleware) para integraÃÃo e compartilhamento inteligente dos recursos (sensores, atuadores e/ou serviÃos) robÃticos identificados por URIs (Uniform Resource Identifiers), empregando a rede TCP/IP, utilizando protocolos com menores restriÃÃes em firewall, uma interface de interaÃÃo humano-mÃquina (IHM) implementada atravÃs de um portal web e uma linguagem de descriÃÃo dos recursos que torna os dados mais portÃveis e interoperÃveis entre diferentes tipos de computadores, sistemas operacionais e linguagens de programaÃÃo. O middleware proposto facilita a computaÃÃo interativa de mÃltiplos aplicativos interconectados com a finalidade de criar uma aplicaÃÃo maior, geralmente distribuÃda sobre uma rede de computadores composta de vÃrios tipos heterogÃneos de hardware e software. Com este modelo de middleware, à possÃvel garantir seguranÃa de acesso aos recursos, abstrair a diversidade do hardware robÃtico, reutilizar a infraestrutura de software para robÃs entre mÃltiplos esforÃos de pesquisa, reduzir o acoplamento entre os mÃltiplos aplicativos, estimular a portabilidade do cÃdigo e suportar escalabilidade da arquitetura. / Middleware is the software layer situated between the operating system and applications layer or between layers of applications, which provides an infrastructure for integrating applications and data in a distributed processing system. In this thesis we propose a new software layer (middleware) for integration and intelligent sharing of robotic resources (sensors, actuators and / or services) identified by URIs (Uniform Resource Identifiers), using the TCP/IP network, employing protocols with minor firewall restrictions and a resource description language that makes data more portable and interoperable between different types of computers, operating systems and programming languages. The proposed middleware facilitates interactive computing of multiple interconnected applications with the purpose to create a larger application, usually distributed over a computer network consisting of various kinds of heterogeneous hardware and software. With this model of middleware, it is possible to ensure security of access to resources, abstracting the diversity of robotic hardware, to reuse the infrastructure of software for robots between multiple search efforts, reduce the coupling between multiple applications, encourage code portability and support scalability of the architecture.

FundaÃÃo Cearense de Apoio ao Desenvolvimento Cientifico e TecnolÃgico / Sistemas de navegaÃÃo autÃnomos devem ser capazes de definir uma seqÃÃncia de aÃÃes a serem tomadas por robÃs mÃveis, dotados de um conjunto limitado de sensores, quando expostos a um ambiente externo suposto desconhecido e tendo que atender simultaneamente a um elenco de objetivos previamente especificados. O interesse cientÃfico no estudo de sistemas de navegaÃÃo em ambientes desconhecidos e sujeitos ao atendimento de mÃltiplos objetivos à motivado basicamente pelo seu evidente potencial em aplicaÃÃes industriais e pelo fato de demandarem a implementaÃÃo de estratÃgias de soluÃÃes complexas. Este trabalho apresenta a modelagem de um sistema de navegaÃÃo para robÃs moveis por meio de Redes de Petri Coloridas. O modelo apresentado consegue simular vÃrias situaÃÃes, tais como a representaÃÃo do mundo em volta do robÃ, interaÃÃo com o ambiente, planejamento de trajetÃria, localizaÃÃo do robà e anÃlise das baterias, bem como servir de base para implementaÃÃo em um robà mÃvel real e otimizaÃÃo do sistema. / Systems of autonomous navigation must be able to define a sequence of actions to be taken bymobile robots endowed with a set of limited sensors, while exposed to an unknown environment and having to serve simultaneously a set of objectives previously specified. The scientific interest in the study of systems of navigation in unknown environment which are subject of serving to several objectives is motivated basically by its evident potential in industrial applications and by the fact of demanding the implementation of complex solutions strategies. This research presents the modeling of a navigation system for mobile robots through coloured Petri nets. The model presented here can simulate several situations, such as: the representation of the world around the robot, interaction with the environment, trajectory planning, robot location, battery analysis, as well as how to serve as a basis for implementation in a real mobile robot and optimization of the system.

Localization and motion control (navigation) are two major tasks for a successful mobile robot navigation. The motion controller determines the appropriate action for the robot’s actuator based on its current state in an operating environment. A robot recognizes its environment through some sensors and executes physical actions through actuation mechanisms. However, sensory information is noisy and hence actions generated based on this information may be non-deterministic. Therefore, a mobile robot provides actions to its actuators with a certain degree of uncertainty. Moreover, when no prior knowledge of the environment is available, the problem becomes even more difficult, as the robot has to build a map of its surroundings as it moves to determine the position. Skilled navigation of a differential drive mobile robot (DDMR) requires solving these tasks in conjunction, since they are inter-dependent. Having resolved these tasks, mobile robots can be employed in many contexts in indoor and outdoor environments such as delivering payloads in a dynamic environment, building safety, security, building measurement, research, and driving on highways. This dissertation exploits the use of the emerging Radio Frequency IDentification (RFID) technology for the design and implementation of cost-effective and modular control techniques for navigating a mobile robot in an indoor environment. A successful realization of this process has been addressed with three separate navigation modules. The first module is devoted to the development of an indoor navigation system with a customized RFID reader. This navigation system is mainly pioneered by mounting a multiple antenna RFID reader on the robot and placing the RFID tags in three dimensional workspace, where the tags’ orthogonal position on the ground define the desired positions that the robot is supposed to reach. The robot generates control actions based on the information provided by the RFID reader for it to navigate those pre-defined points. On the contrary, the second and third navigation modules employ custom-made RFID tags (instead of the RFID reader) which are attached at different locations in the navigation environment (on the ceiling of an indoor office, or on posts, for instance). The robot’s controller generates appropriate control actions for it’s actuators based on the information provided by the RFID tags in order to reach target positions or to track pre-defined trajectory in the environment. All three navigation modules were shown to have the ability to guide a mobile robot in a highly reverberant environment with variant degrees of accuracy.

In the future, robots will expand from industrial and research applications to the home. Domestic service robots will work in the home to perform useful tasks such as object retrieval, cleaning, organization, and security. The tireless support of these systems will not only enable able bodied people to avoid mundane chores; they will also enable the elderly to remain independent from institutional care by providing service, safety, and companionship. Robots will need to understand the relationship between objects and their environments to perform some of these tasks. Structured indoor environments are organized according to architectural guidelines and convenience for their residents. Utilizing this information makes it possible to predict the location of objects. Conversely, one can also predict the function of a room from the detection of a few objects within a given space. This thesis introduces a framework for combining object permanence and context called the probabilistic cognitive model. This framework combines reasoning about spatial extent of places and the identity of objects and their relationships to one another and to the locations where they appear. This type of reasoning takes into account the context in which objects appear to determine their identity and purpose. The probabilistic cognitive model combines a mapping system called OmniMapper with a conditional random field probabilistic model for context representation. The conditional random field models the dependencies between location and identity in a real-world domestic environment. This model is used by mobile robot systems to predict the effects of their actions during autonomous object search tasks in unknown environments.